
We present the CDSConv that learns dynamic scale features guided by the normal curvature of image surface. This operation is implemented by approximating surface normal curvatures in several candidate scales and choosing a proper scale via a classification network.

We propose a new feature extraction network, CDSFNet, which is composed of multiple CDSConv layers for learning robust representation at pixel level. CDFSNet estimates the optimal pixel’s scale to learn features. The scale is selected adaptively with respect to structures, textures, and epipolar constraints.

We present CDS-MVSNet for MVS depth estimation. CDS-MVSNet performs accurate stereo matching and cost aggregation by handling the ambiguity and visibility in the image matching process. It also significantly reduces the run-time and memory consumption by processing the half-resolution images while maintaining the reconstruction quality.

We verify the effectiveness of our method on two benchmark datasets; DTU [1]} and Tanks & Temples [2]}. The results demonstrate that the proposed feature learning method boosts the performance of MVS reconstruction.

